Dataset Information

Accuracy and application of the motif expression decomposition method in dissecting transcriptional regulation.

ABSTRACT: Understanding transcriptional regulation is a major goal of molecular biology. Motif expression decomposition (MED) was recently introduced to describe the expression level of a gene as the sum of the products of the binding strengths of its cis-regulatory motifs and the activities of the corresponding trans-acting transcription factors (TFs). Here, we use computer simulation to examine the accuracy of MED. We found that although MED accurately rebuilds gene expression levels from decomposed motif binding strengths and TF activities, estimates of motif binding strengths and TF activities are unreliable. Nonetheless, MED provides accurate estimates of relative binding strengths of the same motif in different genes and relative activities of the same TF under different conditions. We found that reasonably accurate results are achievable with genome-wide expression data from only 30 conditions and that MED results are robust to the existence of unknown occurrences of known motifs, although they are less robust to the presence of unknown motifs. With these understandings, judicious use of MED will likely provide useful information about eukaryotic transcriptional regulation. As an example, MED results are used to demonstrate that motifs generally have higher binding strengths when appearing in multiple copies than appearing in one copy per promoter.

SUBMITTER: Zhang Z

PROVIDER: S-EPMC2425491 | biostudies-literature | 2008 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Accuracy and application of the motif expression decomposition method in dissecting transcriptional regulation.

Zhang Zhihua Z Zhang Jianzhi J

Nucleic acids research 20080414 10

Understanding transcriptional regulation is a major goal of molecular biology. Motif expression decomposition (MED) was recently introduced to describe the expression level of a gene as the sum of the products of the binding strengths of its cis-regulatory motifs and the activities of the corresponding trans-acting transcription factors (TFs). Here, we use computer simulation to examine the accuracy of MED. We found that although MED accurately rebuilds gene expression levels from decomposed mot ...[more]

PMID: 18411204

Similar Datasets

Project description:BackgroundGenes work coordinately as gene modules or gene networks. Various computational approaches have been proposed to find gene modules based on gene expression data; for example, gene clustering is a popular method for grouping genes with similar gene expression patterns. However, traditional gene clustering often yields unsatisfactory results for regulatory module identification because the resulting gene clusters are co-expressed but not necessarily co-regulated.ResultsWe propose a novel approach, motif-guided sparse decomposition (mSD), to identify gene regulatory modules by integrating gene expression data and DNA sequence motif information. The mSD approach is implemented as a two-step algorithm comprising estimates of (1) transcription factor activity and (2) the strength of the predicted gene regulation event(s). Specifically, a motif-guided clustering method is first developed to estimate the transcription factor activity of a gene module; sparse component analysis is then applied to estimate the regulation strength, and so predict the target genes of the transcription factors. The mSD approach was first tested for its improved performance in finding regulatory modules using simulated and real yeast data, revealing functionally distinct gene modules enriched with biologically validated transcription factors. We then demonstrated the efficacy of the mSD approach on breast cancer cell line data and uncovered several important gene regulatory modules related to endocrine therapy of breast cancer.ConclusionWe have developed a new integrated strategy, namely motif-guided sparse decomposition (mSD) of gene expression data, for regulatory module identification. The mSD method features a novel motif-guided clustering method for transcription factor activity estimation by finding a balance between co-regulation and co-expression. The mSD method further utilizes a sparse decomposition method for regulation strength estimation. The experimental results show that such a motif-guided strategy can provide context-specific regulatory modules in both yeast and breast cancer studies.

Dataset Information

Accuracy and application of the motif expression decomposition method in dissecting transcriptional regulation.

Publications

Accuracy and application of the motif expression decomposition method in dissecting transcriptional regulation.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets